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  • AI refers to "Artificial Intelligence" that refers to different systems or machines that impersonate human intelligence to execute tasks and can iteratively enhance themselves based on the information they accumulate. AI is basically implemented in several forms. It is one sort of a technology that has a never-ending demand. And if you also want to establish your career in this field, taking up a professional course will be beneficial for your career.
  • And if you have no idea where to start, Croma Campus is the best place for you to know about AI right from the beginning. Yes, it's being considered one of the best Artificial Intelligence Training Institute in Gurgaon, and imbibing information from this institution will be fruitful for your career.

Artificial Intelligence Training in Gurgaon

About-Us-Cources

  • To understand every bit of Artificial Intelligence in a much better way. You need to get started with its legit Artificial Intelligence Training. In this case, approaching Croma Campus will be beneficial for you as you will end up learning everything in quite an explained manner.
  • Let’s have a look at what you will end up imbibing at the completion of this course.
    • Basic information regarding Artificial intelligence, and Neural networks.

      You will also receive sessions regarding Pattern recognition, Algorithms, Machine Learning, Language processing.

      Our qualified trainers will also help you know about Robotics, Automata, Deep learning, Sensors, and Programming language.

      Along with imbibing detailed information concerning the main section, and sub-section of this subject, you will also cover some general topics like-Engineering - custom made for CS students, Biology for CS students, Math, Physics - for CS students, Design, and Object-oriented application respectively.

  • The salary package in this field is high right from the beginning level. So, if you are worried about the salary package, then you shouldn't stress about it and rather start brushing up your skills to imbibe more skills. A proper certification of Artificial Intelligence Training in Gurgaon will therefore help you in grabbing a higher package.
    • Well, as a fresher AI Developer, you will earn around Rs. 8 Lakhs per year in the beginning.

      Further, you will earn around even higher salary around Rs. 50 Lakhs annually.

      Well, later in your career this salary structure will get expanded by imbibing more skills, and the latest information.

      Post having this skill, you will be able to enter into a multi-national company that will also offer you a quite higher salary package.

  • Well, there are various AI jobs with skilled professionals to fill them, and as we know shortly, the AI world has shown no signs of slowing down, so the demand is genuinely very high. So, if your interest also lies in this field, opting for Artificial Intelligence Training in Gurgaon will be beneficial for your career.
    • By getting started with Artificial Intelligence Training in Gurgaon, you will turn into a knowledgeable AI Developer.

      Enrolling in this course will allow you to examine this subject right from the scratch.

      By knowing every side of Artificial Intelligence Training in Gurgaon, you will end up acquiring a higher position.

      Your basic AI knowledge will also get strengthened.

      You will have numerous international jobs offers in hand as well.

  • An AI Developer is supposed to execute a wide series of tasks. So, if you also want to turn into a skilled AI Developer, by getting associated with Artificial Intelligence Training Institute in Gurgaon will be appropriate for your career.
    • Your first duty will be to solve numerous business challenges using AI software.

      You will also have to design, build, implement, and monitor AI systems.

      Furthermore, you need to also describe to project managers and stakeholders the potential and limitations of AI systems.

      You will also have to develop data ingest and data transformation architecture.

      Moreover, you might also have to look out for new AI technologies to imply within the business.

  • In the present scenario, you will find numerous reasons to get started with this specific course. Moreover, by obtaining information concerning Artificial Intelligence Training in Gurgaon, you will grow in this field quicker and acquire a higher position as well. By associating with a proper Artificial Intelligence Training Institute in Gurgaon, you will get the chance to know some of the main reasons to learn this course.
    • One of the main reasons is its hugely increasing demand for skilled professionals in AI Developers.

      Moreover, it has a wide bright future ahead.

      By knowing this technology, you will be able to integrate complicated tasks without the need of cost delays respectively.

      You will get a series of job roles in this field as well.

      Immense openings for skilled developers in huge companies.

  • Presently, you will find various companies hiring skilled AI Developers. So, if you’re aim is to acquire a job post completing the Artificial Intelligence Training in Gurgaon, then you genuinely don't have to worry at all as you will surely end up getting into a well-established as there’s genuinely a huge space for skilled candidates, and we will also set your interviews with established companies. So, getting in touch with Artificial Intelligence Training Institute in Gurgaon will only uplift your career graph.
    • Precily Private Limited, Iora Ecological Solutions, Gauge Data Solutions Pvt Ltd, etc. are some of the well-established companies hiring skilled AI Developers.

      Our faculty members will help you in clearing the interview by often conducting a mock test.

      The main agenda of Artificial Intelligence Training in Gurgaon is to assist you to get settled in a well-established workplace.

  • For the past few years, Croma Campus has been considered the best provider of Artificial Intelligence Training in Gurgaon. It basically aims at producing qualitative training along with enough study material and numerous instances. So, if you are also looking to acquire detailed information concerning Artificial Intelligence, getting associated with Croma Campus will be an ideal move toward your career.
    • Getting in touch with us will give you ample chances to obtain the latest information concerning the Artificial Intelligence course.

      Here, you will obtain information regarding its related course.

      Croma Campus will offer you placement assistance.

      Well, right from the beginning, our faculty members will give you suggestive tips to clear the interview process.

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Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
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  • With our AZ-900 “Microsoft Azure fundamentals” certification Training you will learn foundational knowledge of cloud services and how those services are provided with Microsoft Azure. The exam is intended for candidates who are just beginning to work with cloud-based solutions and services or are new to Azure.
  • In this program you will learn:
    • Python Statistics for AI

      Python - MySQL

      Data Science Professional Program

      Machine Learning

      Live Projects

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  • Introduction To Python:
    • Installation and Working with Python

      Understanding Python variables

      Python basic Operators

      Understanding the Python blocks.

  • Introduction To Variables:
    • Variables, expression condition and function

      Global and Local Variables in Python

      Packing and Unpacking Arguments

      Type Casting in Python

      Byte objects vs. string in Python

      Variable Scope

  • Python Data Type:
    • Declaring and usingNumeric data types

      Using stringdata type and string operations

      Understanding Non-numeric data types

      Understanding the concept of Casting and Boolean.

      Strings

      List

      Tuples

      Dictionary

      Sets

  • Introduction Keywords and Identifiers and Operators
    • Python Keyword and Identifiers

      Python Comments, Multiline Comments.

      Python Indentation

      Understating the concepts of Operators

  • Data Structure
    • List

      • What is List.
      • List Creation
      • List Length
      • List Append
      • List Insert
      • List Remove
      • List Append & Extend using “+” and Keyword
      • List Delete
      • List related Keyword in Python
      • List Revers
      • List Sorting
      • List having Multiple Reference
      • String Split to create a List
      • List Indexing
      • List Slicing
      • List count and Looping
      • List Comprehension and Nested Comprehension

      Dictionary

      • Dict Creation
      • Dict Access (Accessing Dict Values)
      • Dict Get Method
      • Dict Add or Modify Elements
      • Dict Copy
      • Dict From Keys.
      • Dict Items
      • Dict Keys (Updating, Removing and Iterating)
      • Dict Values
      • Dict Comprehension
      • Default Dictionaries
      • Ordered Dictionaries
      • Looping Dictionaries
      • Dict useful methods (Pop, Pop Item, Str , Update etc.)
  • Sets, Tuples and Looping Programming
    • Sets

      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
      • Frozen Sets

      Tuple

      • What is Tuple
      • Tuple Creation
      • Accessing Elements in Tuple
      • Changinga Tuple
      • TupleDeletion
      • Tuple Count
      • Tuple Index
      • TupleMembership
      • TupleBuilt in Function (Length, Sort)

      Control Flow

      • Loops
      • Loops and Control Statements (Continue, Break and Pass).
      • Looping techniques in Python
      • How to use Range function in Loop
      • Programs for printing Patterns in Python
      • How to use if and else with Loop
      • Use of Switch Function in Loop
      • Elegant way of Python Iteration
      • Generator in Python
      • How to use nested IF and Else in Python
      • How to use nested Loop in Python
      • Use If and Else in for and While Loop
      • Examples of Looping with Break and Continue Statements
      • How to use IN or NOTkeywordin Python Loop.
  • Exception and File Handling, Module, Function and Packages
    • Python Exception Handling

      • Python Errors and Built-in-Exceptions
      • Exception handing Try, Except and Finally
      • Catching Exceptions in Python
      • Catching Specific Exception in Python
      • Raising Exception
      • Try and Finally

      Python File Handling

      • Opening a File
      • Python File Modes
      • Closing File
      • Writing to a File
      • Reading from a File
      • Renaming and Deleting Files in Python
      • Python Directory and File Management
      • List Directories and Files
      • Making New Directory
      • Changing Directory

      Python Function, Modules and Packages

      • Python Syntax
      • Function Call
      • Return Statement
      • Write an Empty Function in Python –pass statement.
      • Lamda/ Anonymous Function
      • *argsand **kwargs
      • Help function in Python
      • Scope and Life Time of Variable in Python Function
      • Nested Loop in Python Function
      • Recursive Function and Its Advantage and Disadvantage
      • Organizing python codes using functions
      • Organizing python projects into modules
      • Importing own module as well as external modules
      • Understanding Packages
      • Programming using functions, modules & external packages
      • Map, Filter and Reduce function with Lambda Function
      • More example of Python Function
  • Data Automation (Excel, SQL, PDF etc)
    • Python Object Oriented Programming—Oops

      • Concept of Class, Object and Instances
      • Constructor, Class attributes and Destructors
      • Real time use of class in live projects
      • Inheritance, Overlapping and Overloading operators
      • Adding and retrieving dynamic attributes of classes
      • Programming using Oops support

      Python Database Interaction

      • SQL Database connection using
      • Creating and searching tables
      • Reading and Storing configinformation on database
      • Programming using database connections

      Reading an excel

      • Reading an excel file usingPython
      • Writing toan excel sheet using Python
      • Python| Reading an excel file
      • Python | Writing an excel file
      • Adjusting Rows and Column using Python
      • ArithmeticOperation in Excel file.
      • Plotting Pie Charts
      • Plotting Area Charts
      • Plotting Bar or Column Charts using Python.
      • Plotting Doughnut Chartslusing Python.
      • Consolidationof Excel File using Python
      • Split of Excel File Using Python.
      • Play with Workbook, Sheets and Cells in Excel using Python
      • Creating and Removing Sheets
      • Formatting the Excel File Data
      • More example of Python Function

      Working with PDF and MS Word using Python

      • Extracting Text from PDFs
      • Creating PDFs
      • Copy Pages
      • Split PDF
      • Combining pages from many PDFs
      • Rotating PDF’s Pages

      Complete Understanding of OS Module of Python

      • Check Dirs. (exist or not)
      • How to split path and extension
      • How to get user profile detail
      • Get the path of Desktop, Documents, Downloads etc.
      • Handle the File System Organization using OS
      • How to get any files and folder’s details using OS
  • Data Analysis & Visualization
    • Pandas

      • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
      • How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
      • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
      • Exploring the Data Plotting, Correlations, and Histograms
      • Getting statistical information about the data Analysis Concepts, Handle the None Values
      • Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
      • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
      • Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
      • Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
      • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
      • Exporting the results to Excel Joins:
      • Python | Pandas Data Frame Inner Join
      • Under sting the properties of Data Frame Left Join (Left Outer Join)
      • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
      • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
      • Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
      • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
      • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

      NumPy

      • Introduction to NumPy: Numerical Python
      • Importing NumPy and Its Properties
      • NumPy Arrays
      • Creating an Array from a CSV
      • Operations an Array from aCSV
      • Operations with NumPy Arrays
      • Two-Dimensional Array
      • Selecting Elements from 1-D Array
      • Selecting Elements from 2-D Array
      • Logical Operation with Arrays
      • Indexing NumPy elements using conditionals
      • NumPy’sMean and Axis
      • NumPy’sMode, Median and Sum Function
      • NumPy’sSort Function and More

      MatPlotLib

      • Bar Chart using Python MatPlotLib
      • Column Chart using Python MatPlotLib
      • Pie Chart using Python MatPlotLib
      • Area Chart using Python MatPlotLib
      • Scatter Plot Chart using Python MatPlotLib
      • Play with Charts Properties Using MatPlotLib
      • Export the Chart as Image
      • Understanding plt. subplots () notation
      • Legend Alignment of Chart using MatPlotLib
      • Create Charts as Image
      • Other Useful Properties of Charts.
      • Complete Understanding of Histograms
      • Plotting Different Charts, Labels, and Labels Alignment etc.

      Introduction to Seaborn

      • Introduction to Seaborn
      • Making a scatter plot with lists
      • Making a count plot with a list
      • Using Pandas with seaborn
      • Tidy vs Untidy data
      • Making a count plot with a Dataframe
      • Adding a third variable with hue
      • Hue and scattera plots
      • Hue and count plots

      Visualizing Two Quantitative Variables

      • Introduction to relational plots and subplots
      • Creating subplots with col and row
      • Customizing scatters plots
      • Changing the size of scatter plot points
      • Changing the style of scatter plot points
      • Introduction to line plots
      • Interpreting line plots
      • Visualizing standard deviation with line plots
      • Plotting subgroups in line plots

      Visualizing a Categorical and a Quantitative Variable

      • Current plots and bar plots
      • Count plots
      • Bar plot with percentages
      • Customizing bar plots
      • Box plots
      • Create and interpret a box plot
      • Omitting outliers
      • Adjusting the whiskers
      • Point plots
      • Customizing points plots
      • Point plot with subgroups

      Customizing Seaborn Plots

      • Changing plot style and colour
      • Changing style and palette
      • Changing the scale
      • Using a custom palette
      • Adding titles and labels: Part 1
      • Face Grids vs. Axes Subplots
      • Adding a title to a face Grid object
      • Adding title and labels: Part 2
      • Adding a title and axis labels
      • Rotating x-tics labels
      • Putting it all together
      • Box plot with subgroups
      • Bar plot with subgroups and subplots
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  • Python - MySQL
    • Introduction to MySQL

      What is the MySQLdb

      How do I Install MySQLdb

      Connecting to the MYSQL

      Selecting a database

      Adding data to a table

      Executing multiple queries

      Exporting and Importing data tables.

      SQL Functions

      • Single Row Functions
      • Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date
  • General Functions
    • Count, Average, Sum, Now etc.

  • Joining Tables
    • Obtaining data from Multiple Tables

      Types of Joins (Inner Join, Left Join, Right Join & Full Join)

      Sub-Queries Vs. Joins

  • Operators (Data using Group Function)
    • Distinct, Order by, Group by, Equal to etc.

  • Database Objects (Constraints & Views)
    • Not Null

      Unique

      Primary Key

      Foreign Key

  • Structural & Functional Database Testing using TOAD Tool
    • SQL Basic

      • SQL Introduction
      • SQL Syntax
      • SQL Select
      • SQL Distinct
      • SQL Where
      • SQL And & Or
      • SQL Order By
      • SQL Insert
      • SQL Update
      • SQL Delete

      SQL Advance

      • SQL Like
      • SQL Wildcards
      • SQL In
      • SQL Between
      • SQL Alias
      • SQL Joins
      • SQL Inner Join
      • SQL Left Join
      • SQL Right Join
      • SQL Full Join
      • SQL Union

      SQL Functions

      • SQL Avg()
      • SQL Count()
      • SQL First()
      • SQL Last()
      • SQL Max()
      • SQL Min()
      • SQL Sum()
      • SQL Group By
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  • Introduction to Data Science
    • What is Analytics & Data Science

      Common Terms in Analytics

      What is data

      Classification of data

      Relevance in industry and need of the hour

      Types of problems and business objectives in various industries

      How leading companies are harnessing the power of analytics

      Critical success drivers

      Overview of analytics tools & their popularity

      Analytics Methodology & problem-solving framework

      List of steps in Analytics projects

      Identify the most appropriate solution design for the given problem statement

      Project plan for Analytics project & key milestones based on effort estimates

      Build Resource plan for analytics project

      Why Python for data science

  • Accessing/Importing and Exporting Data
    • Importing Data from various sources (Csv, txt, excel, access etc)

      Database Input (Connecting to database)

      Viewing Data objects - sub setting, methods

      Exporting Data to various formats

      Important python modules: Pandas

  • Data Manipulation: Cleansing - Munging Using Python Modules
    • Cleansing Data with Python

      Filling missing values using lambda function and concept of Skewness.

      Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

      Normalizing data

      Feature Engineering

      Feature Selection

      Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

      Label encoding/one hot encoding

  • Data Analysis: Visualization Using Python
    • Introduction exploratory data analysis

      Descriptive statistics, Frequency Tables and summarization

      Univariate Analysis (Distribution of data & Graphical Analysis)

      Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

      Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)

      Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

  • Introduction to Statistics
    • Descriptive Statistics

      Sample vs Population Statistics

      Random variables

      Probability distribution functions

      Expected value

      Normal distribution

      Gaussian distribution

      Z-score

      Central limit theorem

      Spread and Dispersion

      Inferential Statistics-Sampling

      Hypothesis testing

      Z-stats vs T-stats

      Type 1 & Type 2 error

      Confidence Interval

      ANOVA Test

      Chi Square Test

      T-test 1-Tail 2-Tail Test

      Correlation and Co-variance

  • Introduction to Predictive Modelling
    • Concept of model in analytics and how it is used

      Common terminology used in Analytics & Modelling process

      Popular Modelling algorithms

      Types of Business problems - Mapping of Techniques

      Different Phases of Predictive Modelling

  • Data Exploration for Modelling
    • Need for structured exploratory data

      EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

      Identify missing data

      Identify outliers’ data

      Imbalanced Data Techniques

  • Data Pre-Processing & Data Mining
    • Data Preparation

      Feature Engineering

      Feature Scaling

      Datasets

      Dimensionality Reduction

      Anomaly Detection

      Parameter Estimation

      Data and Knowledge

      Selected Applications in Data Mining

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  • Introduction to Machine Learning
    • Artificial Intelligence

      • AI overview
      • Meaning, scope, and 3 stages of AI
      • Decoding AI
      • Features of AI
      • Applications of AI
      • Image recognition
      • Effect of AI on society
      • AI for industries
      • Overview of machine learning
      • ML and AI relationship

      Machine Learning

      Techniques of Machine Learning

      Machine Learning Algorithms

      Algorithmic models of Learning

      Applications of Machine Learning

      Large Scale Machine Learning

      Computational Learning theory

      Reinforcement Learning

  • Supervised Machine Learning
    • Supervised Learning

      • What is Supervised Learning
      • Algorithms in Supervised learning
      • Regression & Classification
      • Regression vs classification
      • Computation of correlation coefficient and Analysis
      • Multivariate Linear Regression Theory
      • Coefficient of determination (R2) and Adjusted R2
      • Model Misspecifications
      • Economic meaning of a Regression Model
      • Bivariate Analysis
      • Naive Bayes classifier, Model Training
      • ANOVA (Analysis of Variance)

      Semi-supervised and Reinforcement Learning

      Bias and variance Trade-off

      Representation Learning

  • Regression
    • Regression and its Types

      Logistic Regression

      Linear Regression

      Polynomial Regression

  • Classification
    • Meaning and Types of Classification

      Nearest Neighbor Classifiers

      K-nearest Neighbors

      Probability and Bayes Theorem

      Support Vector Machines

      Naive Bayes

      Decision Tree Classifier

      Random Forest Classifier

  • Unsupervised Learning: Clustering
    • About Clustering

      Clustering Algorithms

      K-means Clustering

      Hierarchical Clustering

      Distribution Clustering

  • Model optimization and Boosting
    • Ensemble approach

      K-fold cross validation

      Grid search cross validation

      Ada boost and XG Boost

  • Introduction to Deep Learning
    • What are the Limitations of Machine Learning

      What is Deep Learning

      Advantage of Deep Learning over Machine learning

      Reasons to go for Deep Learning

      Real-Life use cases of Deep Learning

  • Deep Learning Networks
    • What is Deep Learning Networks

      Why Deep Learning Networks

      How Deep Learning Works

      Feature Extraction

      Working of Deep Network

      Training using Backpropagation

      Variants of Gradient Descent

      Types of Deep Networks

      Feed forward neural networks (FNN)

      Convolutional neural networks (CNN)

      Recurrent Neural networks (RNN)

      Generative Adversal Neural Networks (GAN)

      Restrict Boltzman Machine (RBM)

  • Deep Learning with Keras
    • Define Keras

      How to compose Models in Keras

      Sequential Composition

      Functional Composition

      Predefined Neural Network Layers

      What is Batch Normalization

      Saving and Loading a model with Keras

      Customizing the Training Process

      Intuitively building networks with Keras

  • Convolutional Neural Networks (CNN)
    • Introduction to Convolutional Neural Networks

      CNN Applications

      Architecture of a Convolutional Neural Network

      Convolution and Pooling layers in a CNN

      Understanding and Visualizing CNN

      Transfer Learning and Fine-tuning Convolutional Neural Networks

  • Recurrent Neural Network (RNN)
    • Intro to RNN Model

      Application use cases of RNN

      Modelling sequences

      Training RNNs with Backpropagation

      Long Short-Term Memory (LSTM)

      Recursive Neural Tensor Network Theory

      Recurrent Neural Network Model

      Time Series Forecasting

  • Natural Language Processing
    • NLP with python

      Bags of words

      Stemming

      Tokenization

      Lemmatization

      TF-IDF

      Sentiment Analysis

      Overview of Tensor Flow

  • What is Tensor Flow
    • Tensor Flow code-basics

      Graph Visualization

      Constants, Placeholders, Variables

      Tensor flow Basic Operations

      Linear Regression with Tensor Flow

      Logistic Regression with Tensor Flow

      K Nearest Neighbor algorithm with Tensor Flow

      K-Means classifier with Tensor Flow

      Random Forest classifier with Tensor Flow

  • Neural Networks Using Tensor Flow
    • Quick recap of Neural Networks

      Activation Functions, hidden layers, hidden units

      Illustrate & Training a Perceptron

      Important Parameters of Perceptron

      Understand limitations of A Single Layer Perceptron

      Illustrate Multi-Layer Perceptron

      Back-propagation – Learning Algorithm

      Understand Back-propagation – Using Neural Network Example

      TensorBoard

  • Introduction to Big Data Hadoop and Spark
    • What is Big Data

      Big Data Customer Scenarios

      Understanding BIG Data: Summary

      Few Examples of BIG Data

      Why BIG data is a BUZZ

      How Hadoop Solves the Big Data Problem

      What is Hadoop

      Hadoop’s Key Characteristics

      Hadoop Cluster and its Architecture

      Hadoop: Different Cluster Modes

      Why Spark is needed

      What is Spark

      How Spark differs from other frameworks

      Spark at Yahoo!

  • BIG Data Analytics and why it’s a Need Now
    • What is BIG data Analytics

      Why BIG Data Analytics is a ‘need’ now

      BIG Data: The Solution

      Implementing BIG Data Analytics – Different Approaches

  • Traditional Analytics vs. BIG Data Analytics
    • The Traditional Approach: Business Requirement Drives Solution Design

      The BIG Data Approach: Information Sources drive Creative Discovery

      Traditional and BIG Data Approaches

      BIG Data Complements Traditional Enterprise Data Warehouse

      Traditional Analytics Platform v/s BIG Data Analytics Platform

  • Big Data Technologies
    • Scala

      • What is Scala
      • Scala in other Frameworks
      • Introduction to Scala REPL
      • Basic Scala Operations
      • Variable Types in Scala
      • Control Structures in Scala
      • Understanding the constructor overloading,
      • Various abstract classes
      • The hierarchy types in Scala,
      • For-each loop, Functions and Procedures
      • Collections in Scala- Array

      Spark

      • Overview to Spark
      • Spark installation, Spark configuration,
      • Spark Components & its Architecture
      • Spark Deployment Modes
      • Limitations of Map Reduce in Hadoop
      • Working with RDDs in Spark
      • Introduction to Spark Shell
      • Deploying Spark without Hadoop
      • Parallel Processing
      • Spark MLLib - Modelling Big Data with Spark
  • Apache Kafka and Flume
    • What is Kafka Why Kafka

      Configuring Kafka Cluster

      Kafka architecture

      Producing and consuming messages

      Operations, Kafka monitoring tool

      Need of Apache Flume

      What is Apache Flume

      Understanding the architecture of Flume

      Basic Flume Architecture

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  • Live Projects
    • Managing credit card Risks

      Bank Loan default classification

      YouTube Viewers prediction

      Super store Analytics (E-commerce)

      Buying and selling cars prediction (like OLX process)

      Advanced House price prediction

      Analytics on HR decisions

      Survival of the fittest

      Twitter Analysis

      Flight price prediction

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures. Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
How Croma Campus Mock Interview Works?

Projects

Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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FAQ's

Croma Campus is the well-known Artificial Intelligence Training in Gurgaon with high tech infrastructure and lab facilities. We also provide online access to servers so that candidates will implement the projects at their homes easily. Croma Campus in Gurgaon mentored more than 3000+ candidates with Artificial Intelligence Training Institute in Gurgaon at a very reasonable fee. The course curriculum is customized as per the requirement of candidates/corporates.

Artificial Intelligence (AI) assistance for the job - Like Data Scientists, Software Analysts and Developers, Computer Engineers and Computer Scientists, Algorithm Specialists, Research Scientists and Engineering consultants, Maintenance Technicians, and Mechanical Engineers.

Croma Campus: Artificial Intelligence Training Institute in Gurgaon conducted by specialist working certified corporate professionals having 8+ years of experience in implementing real-time Artificial Intelligence projects. Candidates will implement the following concepts under Artificial Intelligence: Fundamentals of Reinforcement Learning, Q-Learning Intuition, Building a Self-Driving Car, Creating the environment, Building an AI on real-time projects along with Artificial Intelligence Placement Training modules like aptitude test preparation, etc.

There are four types of Artificial Intelligence: reactive machines, limited memory, the theory of mind and self-awareness.

The ways to connect Croma Campus.

  • Phone number: +91-120-4155255, +91-9711526942
  • Email: info@cromacampus.com
  • Address: G-21, Sector-03, Noida (201301)

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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Training Features

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Instructor-led Sessions

The most traditional way to learn with increased visibility,monitoring and control over learners with ease to learn at any time from internet-connected devices.

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Real-life Case Studies

Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.

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Assignment

Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.

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Lifetime Access

Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.

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24 x 7 Expert Support

With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.

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Certification

Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.

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Your certificate and skills are vital to the extent of jump-starting your career and giving you a chance to compete in a global space.

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